Step 1: Set Up Terraform Configuration
Create a new directory for your Terraform project and navigate into it:

bash
mkdir terraform-rds-example
cd terraform-rds-example

Create a main.tf file where you will define your Terraform configuration:

hcl
provider "aws" {
region = "us-east-1" # Adjust the region as per your preference
}

resource "aws_db_instance" "example_rds" {
identifier = "example-db"
allocated_storage = 20
storage_type = "gp2"
engine = "mysql"
engine_version = "5.7"
instance_class = "db.t2.micro"
name = "exampledb"
username = "admin"
password = "yourpassword" # Replace with a secure password
parameter_group_name = "default.mysql5.7"

tags = {
Name = "example-db"
}
}

Step 2: Initialize and Apply Terraform Configuration
Initialize Terraform in your project directory to download the AWS provider plugin:

bash
terraform init

Validate and apply the Terraform configuration to create the resources:
terraform validate
terraform apply

Terraform will show you the execution plan. Review it and type yes to apply the changes. It will provision the RDS instance defined in main.tf.

Step 3: Deploy Your Website
After Terraform successfully creates the RDS instance, you can proceed to deploy your website. This typically involves setting up your web application on an EC2 instance or using a serverless approach like AWS Lambda or ECS, depending on your application architecture.

Step 4: Configure Your Website to Use RDS
Update your website's configuration to connect to the RDS instance. Use the endpoint and credentials specified in your Terraform configuration (aws_db_instance.example_rds.endpoint, aws_db_instance.example_rds.username, aws_db_instance.example_rds.password).

Step 5: Destroy Resources (Optional)
If you want to tear down the resources created by Terraform (e.g., after testing), you can run:

bash
terraform destroy

Conclusion
Using Terraform to deploy a website with AWS RDS provides a scalable and repeatable approach to managing your infrastructure. Make sure to customize the configuration (main.tf) according to your specific requirements, such as adjusting instance sizes, storage, and other parameters based on your workload and performance needs.